System and method of providing visualization explanations

ABSTRACT

In some example embodiments, an indication of a selected data point of a current visualization can be received. A context of the selected data point can be determined based on a dimension of the data point, and explanation candidates can be generated based on the context of the selected data point. Each exploration candidate can have a different dimension context that is within the context of the selected data point and a corresponding value for the dimension context. For each one of the explanation candidates, a corresponding score can be generated based on a difference between the value for the explanation candidate and an average value of all the values of the explanation candidates. The explanation candidates can be ranked based on the scores. At least one of the explanation candidates can be selected based on the ranking, and selectable explanation(s) for the selected explanation candidate(s) can be displayed.

TECHNICAL FIELD

The present application relates generally to the technical field of dataprocessing, and, in various embodiments, to systems and methods ofproviding visualization explanations.

BACKGROUND

In conventional data analysis tools, it can be difficult for analystsand business users to know what factors are driving or contributing toparticular data points when navigating or exploring data. As a result,significant information is often overlooked.

BRIEF DESCRIPTION OF THE DRAWINGS

Some example embodiments of the present disclosure are illustrated byway of example and not limitation in the figures of the accompanyingdrawings, in which like reference numbers indicate similar elements, andin which:

FIG. 1 is a network diagram illustrating a client-server system, inaccordance with some example embodiments;

FIG. 2 is a block diagram illustrating enterprise applications andservices in an enterprise application platform, in accordance with someexample embodiments;

FIG. 3 illustrates a system architecture for providing visualizationexplanations, in accordance with some example embodiments;

FIGS. 4A-4B illustrate a user interface displaying a currentvisualization and visualization explanations, in accordance with someexample embodiments;

FIG. 5 is a block diagram illustrating components of a visualizationexplanation system, in accordance with some example embodiments;

FIG. 6 is an activity diagram illustrating an activity flow of anexplanation service, in accordance with some example embodiments;

FIG. 7 is a flowchart illustrating a method of providing visualizationexplanations, in accordance with some example embodiments;

FIG. 8 is a block diagram illustrating a mobile device, in accordancewith some example embodiments; and

FIG. 9 is a block diagram of an example computer system on whichmethodologies described herein can be executed, in accordance with someexample embodiments.

DETAILED DESCRIPTION

Example methods and systems of providing visualization explanations aredisclosed. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of example embodiments. It will be evident, however, toone skilled in the art that the present embodiments can be practicedwithout these specific details.

The present disclosure provides features that enable users to select aspecific data point of a current visualization and receivecontextualized explanations that highlight the contributing factorsbehind that data point. The explanations can be displayed as key pointsplus some amount of additional information that provides context for thekey points. Different dimension slices can be used to show the mostimpactful contributors behind a value of the selected data point.

In some example embodiments, an indication of a selected data point of acurrent visualization can be received. The selected data point can beone of a plurality of data points of the current visualization. Thecurrent visualization can comprise a graphical representation of theplurality of data points, and each one of the plurality of data pointscan have a corresponding dimension and measure. A context of theselected data point can be determined based on the correspondingdimension of the data point. A plurality of explanation candidates canbe generated based on the context of the selected data point, with eachone of the plurality of exploration candidates having a differentdimension context that is within the context of the selected data point,as well as a corresponding value for the dimension context. For each oneof the plurality of explanation candidates, a corresponding score can begenerated based on a difference between the corresponding value for thecorresponding explanation candidate and an average value of all of thecorresponding values of the plurality of explanation candidates. Aranking of the plurality of explanation candidates can be generatedbased on the scores. At least one of the explanation candidates can beselected based on the ranking A corresponding selectable explanation foreach one of the selected explanation candidate(s) can be caused to bedisplayed to the user in a graphical user interface of a deviceconcurrently with the current visualization.

In some example embodiments, receiving the indication comprisesreceiving a user-generated interrupt comprising the indication, with theuser-generated interrupt being based on a user selection of the datapoint. The current visualization can be caused to be displayed in afirst dedicated section of the graphical user interface for currentvisualizations, and the corresponding selectable explanation for eachone of the selected at least one explanation candidate can be caused tobe displayed in a second dedicated section of the graphical userinterface for explanations. A user selection of the correspondingselectable explanation of one of the selected explanation candidate(s)can be received, and a graphical representation corresponding to theselected explanation can be caused to be displayed as a subsequentcurrent visualization in the first dedicated section.

In some example embodiments, the current visualization can comprise achart. Each measure of the plurality of data points can comprise anumeric value.

In some example embodiments, each corresponding value for the pluralityof explanation candidates can comprise a maximum value for thecorresponding dimension context of the corresponding explanationcandidate. Generating the plurality of explanation candidates cancomprise applying a tuple of the selected data point of the currentvisualization as a filter.

The methods or embodiments disclosed herein may be implemented as acomputer system having one or more modules (e.g., hardware modules orsoftware modules). Such modules may be executed by one or moreprocessors of the computer system. In some example embodiments, anon-transitory machine-readable storage device can store a set ofinstructions that, when executed by at least one processor, causes theat least one processor to perform the operations and method stepsdiscussed within the present disclosure.

FIG. 1 is a network diagram illustrating a client-server system 100, inaccordance with some example embodiments. A platform (e.g., machines andsoftware), in the example form of an enterprise application platform112, provides server-side functionality, via a network 114 (e.g., theInternet) to one or more clients. FIG. 1 illustrates, for example, aclient machine 116 with programmatic client 118 (e.g., a browser), asmall device client machine 122 with a small device web client 120(e.g., a browser without a script engine), and a client/server machine117 with a programmatic client 119.

Turning specifically to the example enterprise application platform 112,web servers 124 and Application Program Interface (API) servers 125 canbe coupled to, and provide web and programmatic interfaces to,application servers 126. The application servers 126 can be, in turn,coupled to one or more database servers 128 that facilitate access toone or more databases 130. The cross-functional services 132 can includerelational database modules to provide support services for access tothe database(s) 130, which includes a user interface library 136. Theweb servers 124, API servers 125, application servers 126, and databaseservers 128 can host cross-functional services 132. The applicationservers 126 can further host domain applications 134.

The cross-functional services 132 provide services to users andprocesses that utilize the enterprise application platform 112. Forinstance, the cross-functional services 132 can provide portal services(e.g., web services), database services and connectivity to the domainapplications 134 for users that operate the client machine 116, theclient/server machine 117 and the small device client machine 122. Inaddition, the cross-functional services 132 can provide an environmentfor delivering enhancements to existing applications and for integratingthird-party and legacy applications with existing cross-functionalservices 132 and domain applications 134. Further, while the system 100shown in FIG. 1 employs a client-server architecture, the embodiments ofthe present disclosure are of course not limited to such anarchitecture, and could equally well find application in a distributed,or peer-to-peer, architecture system.

The enterprise application platform 112 can implement partition leveloperation with concurrent activities. For example, the enterpriseapplication platform 112 can implement a partition level lock, a schemalock mechanism, manage activity logs for concurrent activity, generateand maintain statistics at the partition level, and efficiently buildglobal indexes. The enterprise application platform 112 is described ingreater detail below in conjunction with FIG. 2.

FIG. 2 is a block diagram illustrating enterprise applications andservices in an enterprise application platform 112, in accordance withan example embodiment. The enterprise application platform 112 caninclude cross-functional services 132 and domain applications 134. Thecross-functional services 132 can include portal modules 140, relationaldatabase modules 142, connector and messaging modules 144, API modules146, and development modules 148.

The portal modules 140 can enable a single point of access to othercross-functional services 132 and domain applications 134 for the clientmachine 116, the small device client machine 122, and the client/servermachine 117. The portal modules 140 can be utilized to process, authorand maintain web pages that present content (e.g., user interfaceelements and navigational controls) to the user. In addition, the portalmodules 140 can enable user roles, a construct that associates a rolewith a specialized environment that is utilized by a user to executetasks, utilize services and exchange information with other users andwithin a defined scope. For example, the role can determine the contentthat is available to the user and the activities that the user canperform. The portal modules 140 include a generation module, acommunication module, a receiving module and a regenerating module. Inaddition the portal modules 140 can comply with web services standardsand/or utilize a variety of Internet technologies including Java, J2EE,SAP's Advanced Business Application Programming Language (ABAP) and WebDynpro, XML, JCA, JAAS, X.509, LDAP, WSDL, WSRR, SOAP, UDDI andMicrosoft .NET.

The relational database modules 142 can provide support services foraccess to the database(s) 130, which includes a user interface library136. The relational database modules 142 can provide support for objectrelational mapping, database independence and distributed computing. Therelational database modules 142 can be utilized to add, delete, updateand manage database elements. In addition, the relational databasemodules 142 can comply with database standards and/or utilize a varietyof database technologies including SQL, SQLDBC, Oracle, MySQL, Unicode,JDBC, or the like.

The connector and messaging modules 144 can enable communication acrossdifferent types of messaging systems that are utilized by thecross-functional services 132 and the domain applications 134 byproviding a common messaging application processing interface. Theconnector and messaging modules 144 can enable asynchronouscommunication on the enterprise application platform 112.

The API modules 146 can enable the development of service-basedapplications by exposing an interface to existing and new applicationsas services. Repositories can be included in the platform as a centralplace to find available services when building applications.

The development modules 148 can provide a development environment forthe addition, integration, updating and extension of software componentson the enterprise application platform 112 without impacting existingcross-functional services 132 and domain applications 134.

Turning to the domain applications 134, the customer relationshipmanagement application 150 can enable access to and can facilitatecollecting and storing of relevant personalized information frommultiple data sources and business processes. Enterprise personnel thatare tasked with developing a buyer into a long-term customer can utilizethe customer relationship management applications 150 to provideassistance to the buyer throughout a customer engagement cycle.

Enterprise personnel can utilize the financial applications 152 andbusiness processes to track and control financial transactions withinthe enterprise application platform 112. The financial applications 152can facilitate the execution of operational, analytical andcollaborative tasks that are associated with financial management.Specifically, the financial applications 152 can enable the performanceof tasks related to financial accountability, planning, forecasting, andmanaging the cost of finance.

The human resource applications 154 can be utilized by enterprisepersonnel and business processes to manage, deploy, and track enterprisepersonnel. Specifically, the human resource applications 154 can enablethe analysis of human resource issues and facilitate human resourcedecisions based on real time information.

The product life cycle management applications 156 can enable themanagement of a product throughout the life cycle of the product. Forexample, the product life cycle management applications 156 can enablecollaborative engineering, custom product development, projectmanagement, asset management and quality management among businesspartners.

The supply chain management applications 158 can enable monitoring ofperformances that are observed in supply chains. The supply chainmanagement applications 158 can facilitate adherence to production plansand on-time delivery of products and services.

The third-party applications 160, as well as legacy applications 162,can be integrated with domain applications 134 and utilizecross-functional services 132 on the enterprise application platform112.

In some example embodiments, features of the present disclosure assistusers during data exploration by providing explanations of selected datapoints of visualizations. Visualizations can comprise graphicalrepresentations of data, such as charts, including measures anddimensions. A measure can be any property on which calculations (e.g.,sum, count, average, minimum, maximum) can be made. A dimension can be astructure that categorizes or labels measures.

The currently active visualization (e.g., the visualization that theuser has generated and is currently viewing) can be the starting pointfor the visualization explanations, with explanation candidates beingdetermined based on a selection of a data point of the currentvisualization. The explanation candidates can be scored and ranked tomaximize the relevancy and interest to the user of the explanations thatare eventually presented to the user.

The visualization explanations can be presented in a side-panel and canbe consumed or ignored as the user wishes. The visualizationexplanations can aid the users when they are stuck and do not know whereto go next in terms of data exploration (e.g., what data to explore),but can also be presented in a peripheral area of the user interface sothat they can be easily ignored by users who already know exactly wherethey want to go in terms of data exploration

FIG. 3 illustrates a system architecture 300 for providing visualizationexplanations, in accordance with some example embodiments. The systemarchitecture 300 can comprise three layers: a client 310, a systemengine 320, and a database 360. In some example embodiments, the client310 is incorporated into one of the client machines 116, 117, or 122 inFIG. 1, the system engine 320 is incorporated into application servers126 in FIG. 1, and the database 360 is incorporated into database(s) 130in FIG. 1. However, it is contemplated that other configurations arealso within the scope of the present disclosure.

A user can use an application 312 on the client 310 to explore data. Theuser can use the application 312 to generate a query to obtain data viaa query component 314 of the application 314. The user can use avisualization component 316 of the application 312 to generate a currentvisualization of the obtained data, such as a chart. The application 312can also comprise a discovery panel component 318 to presentvisualization explanations to the user concurrently with the currentvisualization. The discovery panel component 318 can requestvisualization explanations from the system engine 320. The system engine320 can comprise an explanation service 330 that is configured togenerate visualization explanations and return them to the discoverypanel component 318 based on the request. The database 360 can store oneor more datasets 364, from which data can be obtained in the generationof the current visualization and visualization explanations.

FIG. 4A illustrates a user interface 400 displaying a currentvisualization 412 and visualization explanations 422 a-422 c, inaccordance with some example embodiments. The current visualization 412can be displayed in a first dedicated section 410 (e.g., a main panel)of the user interface 400 that is dedicated to current visualizations412, and the visualization explanations 422 a-422 c can be displayed ina second dedicated section 420 (e.g., a side panel) of the userinterface 400 that is dedicated to visualization explanations 422 a-422c. As will be discussed in further detail below, the visualizationexplanations 422 a-422 c can be selectable by a user (e.g., by a userclicking or tapping on them via the user interface 400).

The current visualization 412 can be a graphical representation of atleast a portion of data of a dataset. The dataset can comprise aplurality of measures and a plurality of dimensions, and the data of thecurrent visualization 412 can comprise at least one measure 414 (e.g.,Total Points in FIG. 4) from the plurality of measures of the datasetand at least one dimension 416 (e.g., Division in FIG. 4) of theplurality of dimensions of the dataset. As previously mentioned, ameasure 414 can be any property on which calculations can be made (e.g.,numeric values), and a dimension 416 can be a structure that categorizesor labels measures 414. The current visualization 412 can comprise agraphical representation of a plurality of data points 418. Each one ofthe plurality of data points 418 can have a corresponding dimension 416and measure 414. The current visualization 412 can comprise a bar chart,a group bar chart, a stacked bar chart, a dual bar chart, a scatterplot, a pie chart, a time chart, a stacked time chart, a dual timechart, a key point chart, or a geographic chart. Other types of chartsand visualizations are also within the scope of the present disclosure.

In the example embodiment of FIG. 4A, the current visualization 412 isbased on a dataset of players in a hockey league and comprises a barchart plotting the sum of points for each division in the hockey league.A data point 418 has been selected, which can be visually indicated bythe selected data point 418 being highlighted as illustrated in FIG. 4A.The selected data point 418 can represent the number of points scored byplayers on teams in the Central Division of the hockey league (e.g., atotal of just over 3000 points, as illustrated in FIG. 4A). In thesecond dedicated section 420, which can be configured and presented as aside panel, a number of different contextual explanations 422 a-422 care displayed to provide additional insight into the selected data point418. The top explanation 422 a indicates that, of the more than 3000points scored by players in the Central Division, 224 points were scoredby players born in Winnipeg, the middle explanation 422 b indicates that631 of the Central Division's points were scored by players fromOntario, and the bottom explanation 422 c indicates that 1,337 of theCentral Division's points were scored by players from Canada.

In response to one of the explanations 422 a-422 c being selected, oneor more values from that selected explanation can be displayed in abroader context. In some example embodiments, a graphical representationof the selected explanation can be displayed as the currentvisualization in the first dedicated section 410. FIG. 4B illustratesone example embodiment of explanation 422 a having been selected (e.g.,clicked by the user), as indicated by explanation 422 a beinghighlighted, and a graphical representation of the explanation 422 abeing displayed as the current visualization 412′ in the first dedicatedsection 410. This subsequent current visualization 412′ (e.g., thecurrent visualization resulting from a previous selection) can have atleast one measure 414′ and at least one dimension 416′. The currentvisualization 412′ can comprise a graphical representation of aplurality of data points 418′. Each one of the plurality of data points418′ can have a corresponding dimension 416′ and measure 414′.Additionally, in some example embodiments, the explanation service 330can determine an mean value of the data points 418′ and cause arepresentation 419 of that mean value to be presented as part of thecurrent visualization 412′. Statistical values other than a mean valuecan also be used, including, but not limited to, median and mode.

As illustrated in FIG. 4B, the data point 418′ representing the 224points for players from Winnipeg can be highlighted in the context ofanother bar chart showing a comparison against other cities, such asToronto, Madison, Stockholm, and the like, as a result of the selectedexplanation 422 a comprising data corresponding to the 224 points forplayers from Winnipeg. In some example embodiments, this subsequentcurrent visualization 412′ (e.g., the second bar chart) is based on afilter being applied to only include data points that fall within thecontext of the Central Division. As a result, the data points 418′ ofthe subsequent current visualization 412′ can be restricted to onlythose data points 418′ that are within the context of the initiallyselected data point 418 in the previous current visualization 418 inFIG. 4A. Based on subsequent current visualization 418′ in FIG. 4B, itcan quickly be seen that the number of Central Division points scored byplayers from Winnipeg far outweighs points scored by players from anyother city. This difference makes Winnipeg an interesting component ofthe number of total points scored in the Central Division.

In some example embodiments, one of the data points 418′ in thesubsequent current visualization 412′ can then be selected, andadditional explanations can be generated and displayed to the user basedon the selected data point 418′. Similar generations and displays ofadditional explanations can be performed for additional subsequentcurrent visualizations.

Referring back to FIG. 3, the explanation service 330 can be configuredto coordinate the retrieval and ranking of visual explanations. In someexample embodiments, a context-appropriate set of transition rules 334can be applied to determine potentially viable explanation candidatesbefore running specific scoring algorithms 336 to determine whichexplanation candidates are the “best” explanations (e.g., whichexplanation candidates to return for display as explanations to theuser).

A transition in the context of the explanation service 330 can be anavigation from one input context to one or more other data pointshaving additional information, or put another way, from a context to anexplanation. The input context can comprise a description or anidentification of a single data point, such as the selected data point.Examples of an input context include, but are not limited to, a bar of abar chart, a section of a pie chart, and a vertex point of a time serieschart.

In order to generate or determine the explanation candidates, theexplanation service 330 can aggregate the context measure across theother dimensions in the dataset in the context of the original selecteddata point, excluding any dimensions known to be ancestors or in a 1:1relationship with the context dimension. The explanation service 330 canuse the maximum values from the aggregation as data point values for theexplanation candidates. These aggregations can be performed in thecontext of the original selected data point, such that the resultingexplanation candidates are all within the context of the selected datapoint that they are explaining. For example, in some exampleembodiments, if a user clicks on a data point representing Canada in2002, any explanations of this selected data point must contain onlydata about Canada in 2002. In other words, in some example embodiments,the tuple of the original selected data point must be applied as a basefilter for all aggregations when searching for explanation candidates touser in providing explanations.

Because the cost of searching through all possible transitions for everyvariation of visualization explanations for the current visualizationcan be computationally expensive, the transition rules can be used toreduce the search space, and thus explanation candidates, to only thosetransitions that are determined to be valuable (e.g., interesting,helpful, relevant, etc.) to the user.

The work of finding explanation candidates or determining their scorescan be extremely database intensive, so performance is an importantconcern. The explanation service 330 can address this concern byperforming two important jobs, query batching and caching. Regardingquery batching, the explanation service 330 can employ a query service340 to perform query batches in retrieving data from one or moredatasets 364 in the database 360 such that table scans (and thereforedata transfer in the system backend) are minimized. Regarding caching,the explanation service 330 can employ a caching logic component 332that manages a shared cache 362 of fully formed visualizationexplanations or explanation candidates in the database 360. After thetransition rules 334 have determined a list of explanation candidatesthat make sense for the provided context, the explanation service 330can retrieve information about any of these explanation candidates thatis stored in the suggestion cache 362, with only the remainder ofexplanation candidates in the list (e.g., explanation candidates notstored in the suggestion cache 362) needing to be data populated andscored.

The explanation service 330 can be invoked by the application component312 in the client layer 310. In some example embodiments, theexplanation service 330 is implemented on a server remote from theclient 310. This configuration can have several benefits. First, itimproves security. Requests can be processed in the current user'scontext. However, there may be scenarios where the orchestration logicmay wish to take a broader view of input data (e.g., dataset statistics,user information (e.g., user type, profession), user preferences, or,potentially, knowledge base data) in order to influence the ranking ofits explanations (even though the final results returned to the client310 can be limited by user rights). The system engine 320 can utilize atechnical user account and freely handle privileged data that could notbe safely passed to the client 310. Second, this configuration enhancesdelivery simplicity. Client libraries typically need to be packaged andinstalled along with their consuming application. A relatively thickclient library potentially increases coupling between the layers.Instead, a single layer can be employed to minimize this coupling and,along with it, minimize the need to update client code when the serverchanges and vice versa. And third, this configuration can minimize datatransfer to the client 310.

The explanation service 330 can have an API comprising a call for anexplanation, such as explain(context, n), which can accept a contextobject that describes a single data point (e.g., a selected data point)or multiple data points (e.g., multiple selected data points, which willbe discussed in further detail later), and can return an array ofexplanations containing enough information to allow the clientapplication 312 to render appropriately. The context object can comprisea tuple path along with a measure field. The API call can also allow theclient 310 to specify a maximum number of explanations to return bypassing in a value for n. The API for retrieving explanations can relyon passing a couple of data structures back and forth. For example, whenmaking a call for an explanation, the client 310 can pass a contextobject describing the starting visualization state from whichexplanations are to be determined. In return, an array of explanationscan be passed back containing enough information for the client 310 torender the explanations as desired.

The context object can be a serializable description of the selecteddata point of the current visualization that is used both as a startingpoint for generating explanation candidates for the selected data point,as well as in the resulting explanations themselves. In some exampleembodiments, the context object can be at a level of abstraction that isindependent from any specific chart rendering technology. Rather, it canbe a general visualization description containing information that isuseful for processing and communicating explanations. In some exampleembodiments, the context object can comprise a visualizationspecification with additional information about the data query. In someexample embodiments, the context object can comprise: Chart Type, ChartGeometry (e.g., ordered lists representing “axes”), Dimension IDs,Measure IDs, and filter and prompting values.

Numeric key data points can be a slightly special type because theyrepresent a single specific value. In this case, Chart Geometry can be aMeasure ID, but can be accompanied by some way to describe the specificdimensional context by which the value is defined. In online analyticalprocessing (OLAP) terms, this can be the “tuple”. In addition to thestructure described above, the visualization context for a key datapoint can also include: Tuple (e.g., an ordered list of dimensionmembers identifying a specific single value context). The tuple can beexpressed as a set of filters, but it can be useful to keep the tupledefinition distinct from any other filters that may be applied.

The return value from the call for explanations can be a list ofexplanation candidates ordered from highest to lowest score. Eachexplanation candidate object can comprise a score (e.g., a value between0 and 1), a context, and data from the corresponding dataset. Theexplanation service 330 can return a variety of different types of data.

In some example embodiments, the explanation service 330 can return fullchart data, thereby giving a guarantee to callers that suggestionresults are internally consistent and fully renderable without requiringany additional data querying steps.

In some example embodiments, the explanation service 330 can return apotentially lower-resolution snapshot of the data appropriate only forproducing a thumbnail. For example, a time series chart with many linescan be reduced to just a few lines, or scatterplots that would be overlydense in a thumbnail could have some data points removed. Limiting thedata in this way has the benefit of guaranteeing that the data sizenever becomes too large. In returning this snapshot of the data, it canbe beneficial for the explanation service 330 to acquire and use certaininformation. For example, the explanation service 330 can acquire anduse information about the resolution of the client 310 in order todetermine what level of low-resolution is appropriate. The explanationservice 330 can acquire chart-specific knowledge about how toappropriately reduce resolution for that particular chart type. Thiscreates another point of contact for extensibility and increasescomplexity.

The explanation service 330 can also return some additional contextinformation to assist the client 310 in displaying an interesting textblurb within each explanation describing some facts about theexplanation. In the example embodiment illustrated in FIG. 4A, the dataattributes Points, City, and Winnipeg can be determined from the contextof the current visualization 412, but the additional context items canbe returned explicitly. One example of additional context informationincludes a qualifier at the beginning of the phrase saying, for example,“High” or “Low”, as well as additional facts, such as a certainpercentage of a total value (e.g., 7% of total) and a certain percentageabove an average value (e.g., 802% above average). An additionalproperty bag for additional information can be returned forexplanations, and can comprise elements including, but not limited to, aqualifying prefix (e.g., High), and different facts (e.g., % of total, %above average). The additional context information can be determined bythe explanation service 330 via a statistical analysis of the data setof the current visualization 412. The explanation service 220 can userules for determining or identifying a “High” value (e.g., the highestvalue for a particular context dimension) or a “Low” value (e.g., thelowest value for a particular context dimension), as well as rules fordetermining or identifying statistical information (e.g., the percentageof a total value that a particular value represents, the percentageabove or below an average value that a particular value represents).

In some example embodiments, some amount of structure can be applied tothe additional information content such that the information can beeasily communicated to the client 310 in a way that allows the client310 to easily convert the information into display strings. Since thereis likely a fairly limited set of possible content types, a simple dataformat that relies on a small number of predefined values forcommunicating these pieces of information can be created.

The explanation service 330 can employ one or more scoring algorithms336 to determine which explanation candidates to return for display tothe user. The scoring algorithms 336 can comprise a statistical analysisperformed on specific data related to the selected data point in orderto produce a score (or some other metric for evaluation) that can becompared against scores for other data related to the selected datapoint.

In some example, embodiments, the score for an explanation candidate canbe based on a difference between a value for the explanation candidateand an average value of values for all of the explanation candidates,with more preference or priority (e.g., more weight) being applied to anexplanation candidate the more a value of that explanation candidate isan outlier, such that a first explanation candidate having a value of aparticular dimension context that is 802% above an average value of thatparticular dimension context for all of the explanation candidates willhave a higher score than a second explanation candidate having a valueof that particular dimension context that is only 230% above the averagevalue of that particular dimension context for all of the explanationcandidates. In some example embodiments, the scoring can be based on howmuch greater than the average value a maximum value for a particulardimension context is, such that the more of an outlier the maximum valueis, the higher the score for the corresponding explanation candidatewill be.

It is contemplated that the use of the term “higher” with respect to theterm “score” can correspond to a likelihood that the correspondingexplanation candidate will be selected rather than just a number value.Depending on how the explanation candidates are to be ranked andselected, what is considered a high score and what is considered a lowscore can vary. For example, a score of 0.9 is a higher number valuethan a score of 0.5. However, if the scoring algorithm is configured toassign a lower number value to an explanation candidate the greater thatexplanation candidate represents an outlier, and if the explanationcandidates with the lowest number values for scores are the most likelyto be selected as explanations, then 0.5 is functionally a “higher”score than 0.9 in this particular example. A direct relationship betweenthe number value of the score and the likelihood that the correspondingexplanation candidate will be selected is also within the scope of thepresent disclosure.

Referring to FIG. 4A, explanation 422 a has a value of 224 for thedimension context of points by players born in Winnipeg, which is withinthe context of the selected data point of players within the CentralDivision. Since this value is the greatest outlier (e.g., 802% above theaverage value) within the different dimension contexts of points byplayers by city of birth (e.g., Winnipeg, Toronto, Madison), thecorresponding explanation 422 a is selected to be displayed to the user.

It is contemplated that factors other than the outlier factor discussedabove can also be used to determine the scores for the explanationcandidates, or to rank or select the explanation candidates after thescores have been determined. In some example embodiments, these factorscan include, but are not limited to, usage data. Usage data can compriseinformation about the use of data, the use of fields of data, or the useof visualizations (e.g., charts) by a user or a group of users. Theexplanation service 330 can use the usage data to give priority toexplanation candidates that use commonly used or popular data, fields ofdata, or types of visualizations. For example, the explanation service330 can store, maintain, and access information indicating the level ofusage (e.g., quantity, frequency) for data, fields of data, or types ofvisualizations. This information can be stored in the database 360.Explanation candidates can be scored, ranked, or selected using thisinformation, with priority being given to explanation candidates havingdata or a field of data or being of a type that has a high level ofusage relative to the other explanation candidates. In some exampleembodiments, the level of usage can be based on the level of usage forthe user to which the current visualization is being displayed. Forexample, if the user has previously selected, or otherwise used, certaindata, then any explanation candidates that include that certain data canbe given additional weight in the scoring or selection of explanationcandidates as explanations (e.g., be given a higher score or higherlikelihood of being selected).

In some example embodiments, the level of usage can be based on thelevel of usage for a group of users with which the user, to which thecurrent visualization is being displayed, is associated. Thisassociation can be based on profile information of the users. Suchprofile information can include, but is not limited to, the user'soccupation, company, organization, geographic information (e.g.,country), usage patterns, age, or gender. For example, if other usershaving similar profile information as the user to which the currentvisualization is being presented typically select, or otherwise use,certain data, then any visualization candidates that include thatcertain data can be given additional weight in the scoring or selectionof explanation candidates as explanations (e.g., be given a higher scoreor higher likelihood of being selected).

In some example embodiments, users can provide explicit feedbackindicating preferences for certain data, fields of data, or types ofvisualizations. This feedback can then be used to affect the scoring orselection of explanation candidates, giving additional weight toexplanation candidates having characteristics similar to those preferredby the user to which the current visualization is being displayed.

Performance of the explanation service 330 can be a challenge, both interms of response time and in terms of system load. In some exampleembodiments, the explanation service 330 can limit the possible searchspace for explanation candidates in order to minimize load. In someexample embodiments, ancestor dimensions (e.g., those dimensions in a1:N relationship with the context dimensions) can be excluded.

Even in the simplest initial implementation, scoring an explanationcandidate can involve calculating the average value for a measure acrossall rows in the dataset, which can be completed quickly inside thedatabase, but necessarily as quick to be completed inside the systemengine 320 where the scoring algorithms 336 can be run. Therefore, insome example embodiments, the scoring calculations can be performed inthe database 360. The scoring algorithm module 336 can indicate the kindof data it needs in order to perform its scoring.

FIG. 5 is a block diagram illustrating components of a visualizationexplanation system 500, in accordance with some example embodiments. Thevisualization explanation system 500 can comprise the components andfunctionality of the explanation service 330 in FIG. 3. In some exampleembodiments, the visualization explanation system 500 can comprise anycombination of one or more of a candidate determination module 510, acandidate scoring module 520, a candidate ranking module 530, avisualization explanation module 540, and one or more databases 360. Themodules 510, 520, 530, and 540 can reside on a machine having a memoryand at least one processor (not shown). In some example embodiments,these modules 510, 520, 530, and 540 can be incorporated into theenterprise application platform 112 in FIG. 1 (e.g., on applicationserver(s) 126). However, it is contemplated that other configurationsare also within the scope of the present disclosure.

The candidate determination module 510 can be configured to receive anindication of a selected data point of a current visualization. Thecurrent visualization can comprise a graphical representation of theplurality of data points. Each one of the plurality of data points canhave a corresponding dimension and measure. The candidate determinationmodule 510 can determine a context of the selected data point based onthe corresponding dimension of the data point, and then generate aplurality of explanation candidates based on the context. Each one ofthe plurality of exploration candidates can have a different dimensioncontext that is within the context of the selected data point, as wellas a corresponding value for the dimension context. In some exampleembodiments, the indication of the selected data point can comprise auser-generated interrupt based on a user selection of the data point. Inother example embodiments, the indication of the selected data point canbe based on an automatic selection of the data point (e.g., without theuser clicking or otherwise selecting the data point). In some exampleembodiments, the candidate determination module 510 is furtherconfigured to apply a tuple of the selected data point of the currentvisualization as a filter in generating the plurality of explanationcandidates.

The candidate scoring module 520 can be configured to generate, for eachone of the plurality of explanation candidates, a corresponding scorebased on a difference between the value for the correspondingexplanation candidate and an average value of the values of theplurality of explanation candidates, as previously discussed. In someexample, embodiments, each corresponding value for the plurality ofexplanation candidates can comprise a maximum value for thecorresponding dimension context of the corresponding explanationcandidate. Furthermore, the candidate scoring module 520 can beconfigured to generate the scores for the plurality of explanationcandidates, additionally or alternatively, based on at least one of:usage data of a data point of the corresponding visualization candidate,usage data of a measure of the corresponding visualization candidate,usage data of a dimension of the corresponding visualization candidate,usage data of a chart type of the corresponding visualization candidate,profile information of the user, and explicit user feedback for a datapoint, measure, dimension, or chart type of the correspondingexplanation candidate.

The candidate ranking module 530 can be configured to generate a rankingof the plurality of explanation candidates based on the scores. Theranking can be in descending order of scores or in ascending order ofscores.

The visualization suggestion module 540 can be configured to select atleast one of the explanation candidates based on the ranking, and causea corresponding selectable explanation for each one of the selectedexplanation candidate(s) to be displayed to a user in a graphical userinterface of a device concurrently with the current visualization. Insome example embodiments, the current visualization can be caused to bedisplayed in a first dedicated section of the graphical user interfacefor current visualizations, and the corresponding selectable explanationfor each one of the selected at least one explanation candidate can becaused to be displayed in a second dedicated section of the graphicaluser interface for explanations. A user selection of the correspondingselectable explanation of one of the selected explanation candidate(s)can be received, and a graphical representation corresponding to theselected explanation can be caused to be displayed as a subsequentcurrent visualization in the first dedicated section. The graphicalrepresentation corresponding to the selected explanation canadditionally or alternatively be caused to be displayed in a new window,tab, dialog box, or other user interface element.

It is contemplated that the scoring and/or selection of the explanationcandidates discussed above can also be based, at least in part, on alevel of variance between the explanation candidates in terms of theircorresponding measure(s) and/or dimension(s). In some embodiments, thevariance of measures and/or dimensions between explanation candidatescan be measured using analytics, and such measurements can be used todetermine the scores for the explanation candidates or to determinewhich explanation candidates to select for use as an explanation to bepresented to the user. In some example embodiments, the scoring orselection process can be configured to favor the use of explanationcandidates having different measure(s) and/or dimension(s). For example,if a first explanation candidate has a first measure, such as “teamname”, that is determined to be equivalent to or have a threshold levelof similarity with a second measure, such as “team ID”, of a secondexplanation candidate, the score of the first explanation candidateand/or the second explanation candidate can be negatively influenced bythis determination, or the explanation service 330 can determine to makea selection between the first explanation candidate and the secondexplanation candidate, but not a selection of both, based on theequivalency or similarity determination, since there is no substantialdifference between the information being presented in the firstexplanation candidate and the second explanation candidate. Similarly,the greater the difference of measures and/or dimensions betweenexplanation candidates is determined to be, the more positively thescoring and/or selection of those explanation candidates can beinfluenced.

Referring back to FIG. 4A, although the example embodiments disclosedtherein illustrates visualization explanations 422 a-422 c beinggenerated and presented based on the selection of a single data point418, it is contemplated that, in other example embodiments,visualization explanations can be generated and presented based on theselection of multiple data points. The visualization explanation system500 can be configured to generate and present explanations for eachindividual selected data point, as well as to generate and presentexplanations that explain one or more differences between multipleselected data points. For example, in FIG. 4A, if a user was to selectboth the data point corresponding to the Atlantic division and the datapoint corresponding to the Northwest division, the visualizationexplanation system 500 can generate and present one or morevisualization explanations that explain the difference between thecorresponding measures of these two selected divisions, for example,explaining one or more factors that caused or influenced the disparitybetween the total points for the Atlantic division, which has thehighest total points, and the Northwest division, which has the lowesttotal points. One example of such an explanation can include, but is notlimited to, the Atlantic division having the two highest scoring teamsin the league, while the Northwest division has the three lowest scoringteams in the league. Other examples and configurations are also withinthe scope of the present disclosure.

It is contemplated that the visualization explanation system 500 canincorporate any of the other features disclosed herein.

FIG. 6 is an activity diagram illustrating an activity flow 600 of anexplanation service, in accordance with some example embodiments. Theoperations of the activity flow 600 can be performed by processing logicthat can comprise hardware (e.g., circuitry, dedicated logic,programmable logic, microcode, etc.), software (e.g., instructions runon a processing device), or a combination thereof. In one exampleembodiment, the activity flow 600 is performed by the explanationservice 330 of FIG. 3 or the visualization explanation system 500 ofFIG. 5, or any combination of one or more of their respective componentsor modules, as described above.

At point 602, a request can be accepted from a client, as previouslydiscussed with respect to FIG. 3. The client request can compriseinformation describing or otherwise indicating details about a currentvisualization being displayed to a user on the client. At point 604,transition rules can be applied to the current visualization in order togenerate explanation candidates, as previously discussed. A search spaceof options for generating explanation candidates can be iterated throughaccording to the transition rules. At point 606, a cache can be checkedto determine whether there are available scores for the explanationcandidates, as previously discussed. If it is determined that there areavailable scores in the cache, then the corresponding scores arereturned, and then the explanation candidates are ranked and slicedbased on their corresponding scores at operation 614, as previouslydiscussed. The selected explanation candidates can then be returned asexplanations to the client at operation 616, as previously discussed.

Referring back to the checking of the cache at operation 606, if it isdetermined that there are not available scores in the cache, then, atoperation 608, a batch query can be executed to obtain data to be usedin scoring the explanation candidates, as previously discussed. Atoperation 610, the explanation candidates can then be scored, aspreviously discussed. At operation 612, the cache can then be updatedwith the scores for the explanation candidates. The scores can then beused to rank and slice the explanation candidates at operation 614, aspreviously discussed. It is contemplated that any of the other featuresdescribed within the present disclosure can be incorporated into theactivity flow 600.

FIG. 7 is a flowchart illustrating a method 700 of providingvisualization explanations, in accordance with some example embodiments.Method 700 can be performed by processing logic that can comprisehardware (e.g., circuitry, dedicated logic, programmable logic,microcode, etc.), software (e.g., instructions run on a processingdevice), or a combination thereof. In one example embodiment, the method700 is performed by the explanation service 330 of FIG. 3 or thevisualization explanation system 500 of FIG. 5, or any combination ofone or more of their respective components or modules, as describedabove.

At operation 710, a current visualization of at least a portion of dataof a dataset can be displayed to a user in a graphical user interface ofa device, and an indication of one or more selected data points of acurrent visualization can be received. The selected data point(s) can beone or more of a plurality of data points of the current visualization.The current visualization can comprise a graphical representation of theplurality of data points, and each one of the plurality of data pointscan have a corresponding dimension and measure.

At operation 720, a context of the selected data point(s) can bedetermined based on the corresponding dimension of the data point(s). Atoperation 730, a plurality of explanation candidates can be generatedbased on the context of the selected data point(s). Each one of theplurality of exploration candidates can have a different dimensioncontext that is within the context of the selected data point(s), aswell as a corresponding value for the dimension context. At operation740, for each one of the plurality of explanation candidates, acorresponding score can be generated based on a difference between thecorresponding value for the corresponding explanation candidate and anaverage value of all of the corresponding values of the plurality ofexplanation candidates. At operation 750, a ranking of the plurality ofexplanation candidates can be generated based on the scores. Forexample, the plurality of explanation candidates can be ranked inascending order or descending order of scores. At operation 760, atleast one of the explanation candidates can be selected based on theranking; For example, the top N-ranked explanation candidates (e.g., Nexplanation candidates with the highest scores) can be selected, where Nis a predetermined number of one or greater.

At operation 770, a corresponding selectable explanation for each one ofthe selected at least one explanation candidate can be caused to bedisplayed to a user in a graphical user interface of a deviceconcurrently with the current visualization. At operation 780, a userselection of the corresponding selectable explanation of one of theselected explanation candidate(s) can be received or detected. Atoperation 790, a graphical representation corresponding to the selectedexplanation can be caused to be displayed as a subsequent currentvisualization in the first dedicated section.

It is contemplated that any of the other features described within thepresent disclosure can be incorporated into method 700.

Example Mobile Device

FIG. 8 is a block diagram illustrating a mobile device 800, according tosome example embodiments. The mobile device 800 can include a processor802. The processor 802 can be any of a variety of different types ofcommercially available processors suitable for mobile devices 800 (forexample, an XScale architecture microprocessor, a Microprocessor withoutInterlocked Pipeline Stages (MIPS) architecture processor, or anothertype of processor). A memory 804, such as a random access memory (RAM),a Flash memory, or other type of memory, is typically accessible to theprocessor 802. The memory 804 can be adapted to store an operatingsystem (OS) 806, as well as application programs 808, such as a mobilelocation enabled application that can provide LBSs to a user. Theprocessor 802 can be coupled, either directly or via appropriateintermediary hardware, to a display 810 and to one or more input/output(I/O) devices 812, such as a keypad, a touch panel sensor, a microphone,and the like. Similarly, in some example embodiments, the processor 802can be coupled to a transceiver 814 that interfaces with an antenna 816.The transceiver 814 can be configured to both transmit and receivecellular network signals, wireless data signals, or other types ofsignals via the antenna 816, depending on the nature of the mobiledevice 800. Further, in some configurations, a GPS receiver 818 can alsomake use of the antenna 816 to receive GPS signals.

Modules, Components and Logic

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules can constitute eithersoftware modules (e.g., code embodied on a machine-readable medium or ina transmission signal) or hardware modules. A hardware module is atangible unit capable of performing certain operations and can beconfigured or arranged in a certain manner. In example embodiments, oneor more computer systems (e.g., a standalone, client, or server computersystem) or one or more hardware modules of a computer system (e.g., aprocessor or a group of processors) can be configured by software (e.g.,an application or application portion) as a hardware module thatoperates to perform certain operations as described herein.

In various embodiments, a hardware module can be implementedmechanically or electronically. For example, a hardware module cancomprise dedicated circuitry or logic that is permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC)) toperform certain operations. A hardware module can also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) can bedriven by cost and time considerations.

Accordingly, the term “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired) or temporarilyconfigured (e.g., programmed) to operate in a certain manner and/or toperform certain operations described herein. Considering embodiments inwhich hardware modules are temporarily configured (e.g., programmed),each of the hardware modules need not be configured or instantiated atany one instance in time. For example, where the hardware modulescomprise a general-purpose processor configured using software, thegeneral-purpose processor can be configured as respective differenthardware modules at different times. Software can accordingly configurea processor, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules can be regarded as being communicatively coupled. Where multipleof such hardware modules exist contemporaneously, communications can beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the hardware modules. In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules can be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware modules have access. Forexample, one hardware module can perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module can then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules can also initiate communications with input oroutput devices and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein can beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors can constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein can, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods described herein can be at least partiallyprocessor-implemented. For example, at least some of the operations of amethod can be performed by one or more processors orprocessor-implemented modules. The performance of certain of theoperations can be distributed among the one or more processors, not onlyresiding within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors canbe located in a single location (e.g., within a home environment, anoffice environment or as a server farm), while in other embodiments theprocessors can be distributed across a number of locations.

The one or more processors can also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations can be performed by a group of computers (as examples ofmachines including processors), these operations being accessible via anetwork (e.g., the network 114 of FIG. 1) and via one or moreappropriate interfaces (e.g., APIs).

Example embodiments can be implemented in digital electronic circuitry,or in computer hardware, firmware, software, or in combinations of them.Example embodiments can be implemented using a computer program product,e.g., a computer program tangibly embodied in an information carrier,e.g., in a machine-readable medium for execution by, or to control theoperation of, data processing apparatus, e.g., a programmable processor,a computer, or multiple computers.

A computer program can be written in any form of programming language,including compiled or interpreted languages, and it can be deployed inany form, including as a stand-alone program or as a module, subroutine,or other unit suitable for use in a computing environment. A computerprogram can be deployed to be executed on one computer or on multiplecomputers at one site or distributed across multiple sites andinterconnected by a communication network.

In example embodiments, operations can be performed by one or moreprogrammable processors executing a computer program to performfunctions by operating on input data and generating output. Methodoperations can also be performed by, and apparatus of exampleembodiments can be implemented as, special purpose logic circuitry(e.g., a FPGA or an ASIC).

A computing system can include clients and servers. A client and serverare generally remote from each other and typically interact through acommunication network. The relationship of client and server arises byvirtue of computer programs running on the respective computers andhaving a client-server relationship to each other. In embodimentsdeploying a programmable computing system, it will be appreciated thatboth hardware and software architectures merit consideration.Specifically, it will be appreciated that the choice of whether toimplement certain functionality in permanently configured hardware(e.g., an ASIC), in temporarily configured hardware (e.g., a combinationof software and a programmable processor), or a combination ofpermanently and temporarily configured hardware can be a design choice.Below are set out hardware (e.g., machine) and software architecturesthat can be deployed, in various example embodiments.

FIG. 9 is a block diagram of a machine in the example form of a computersystem 900 within which instructions 924 for causing the machine toperform any one or more of the methodologies discussed herein can beexecuted, in accordance with some example embodiments. In alternativeembodiments, the machine operates as a standalone device or can beconnected (e.g., networked) to other machines. In a networkeddeployment, the machine can operate in the capacity of a server or aclient machine in a server-client network environment, or as a peermachine in a peer-to-peer (or distributed) network environment. Themachine can be a personal computer (PC), a tablet PC, a set-top box(STB), a Personal Digital Assistant (PDA), a cellular telephone, a webappliance, a network router, switch or bridge, or any machine capable ofexecuting instructions (sequential or otherwise) that specify actions tobe taken by that machine. Further, while only a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute a set (ormultiple sets) of instructions to perform any one or more of themethodologies discussed herein.

The example computer system 900 includes a processor 902 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU) orboth), a main memory 904 and a static memory 906, which communicate witheach other via a bus 908. The computer system 900 can further include avideo display unit 910 (e.g., a liquid crystal display (LCD) or acathode ray tube (CRT)). The computer system 900 also includes analphanumeric input device 912 (e.g., a keyboard), a user interface (UI)navigation (or cursor control) device 914 (e.g., a mouse), a disk driveunit 916, a signal generation device 918 (e.g., a speaker) and a networkinterface device 920.

The disk drive unit 916 includes a machine-readable medium 922 on whichis stored one or more sets of data structures and instructions 924(e.g., software) embodying or utilized by any one or more of themethodologies or functions described herein. The instructions 924 canalso reside, completely or at least partially, within the main memory904 and/or within the processor 902 during execution thereof by thecomputer system 900, the main memory 904 and the processor 902 alsoconstituting machine-readable media. The instructions 924 can alsoreside, completely or at least partially, within the static memory 906.

While the machine-readable medium 922 is shown in an example embodimentto be a single medium, the term “machine-readable medium” can include asingle medium or multiple media (e.g., a centralized or distributeddatabase, and/or associated caches and servers) that store the one ormore instructions 924 or data structures. The term “machine-readablemedium” shall also be taken to include any tangible medium that iscapable of storing, encoding or carrying instructions for execution bythe machine and that cause the machine to perform any one or more of themethodologies of the present embodiments, or that is capable of storing,encoding or carrying data structures utilized by or associated with suchinstructions. The term “machine-readable medium” shall accordingly betaken to include, but not be limited to, solid-state memories, andoptical and magnetic media. Specific examples of machine-readable mediainclude non-volatile memory, including by way of example semiconductormemory devices (e.g., Erasable Programmable Read-Only Memory (EPROM),Electrically Erasable Programmable Read-Only Memory (EEPROM), and flashmemory devices); magnetic disks such as internal hard disks andremovable disks; magneto-optical disks; and compact disc-read-onlymemory (CD-ROM) and digital versatile disc (or digital video disc)read-only memory (DVD-ROM) disks.

The instructions 924 can further be transmitted or received over acommunications network 926 using a transmission medium. The instructions924 can be transmitted using the network interface device 920 and anyone of a number of well-known transfer protocols (e.g., HTTP). Examplesof communication networks include a LAN, a WAN, the Internet, mobiletelephone networks, POTS networks, and wireless data networks (e.g.,WiFi and WiMax networks). The term “transmission medium” shall be takento include any intangible medium capable of storing, encoding, orcarrying instructions for execution by the machine, and includes digitalor analog communications signals or other intangible media to facilitatecommunication of such software.

Although an embodiment has been described with reference to specificexample embodiments, it will be evident that various modifications andchanges can be made to these embodiments without departing from thebroader spirit and scope of the present disclosure. Accordingly, thespecification and drawings are to be regarded in an illustrative ratherthan a restrictive sense. The accompanying drawings that form a parthereof, show by way of illustration, and not of limitation, specificembodiments in which the subject matter can be practiced. Theembodiments illustrated are described in sufficient detail to enablethose skilled in the art to practice the teachings disclosed herein.Other embodiments can be utilized and derived therefrom, such thatstructural and logical substitutions and changes can be made withoutdeparting from the scope of this disclosure. This Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement calculated toachieve the same purpose can be substituted for the specific embodimentsshown. This disclosure is intended to cover any and all adaptations orvariations of various embodiments. Combinations of the aboveembodiments, and other embodiments not specifically described herein,will be apparent to those of skill in the art upon reviewing the abovedescription.

What is claimed is:
 1. A system comprising: a candidate determination module, executable on at least one processor, configured to: receive an indication of a selected data point of a current visualization, the current visualization comprising a graphical representation of the plurality of data points, each one of the plurality of data points having a corresponding dimension and measure; determine a context of the selected data point based on the corresponding dimension of the data point; and generate a plurality of explanation candidates based on the context, each one of the plurality of exploration candidates having a different dimension context that is within the context of the selected data point and a corresponding value for the dimension context; a candidate scoring module configured to generate, for each one of the plurality of explanation candidates, a corresponding score based on a difference between the value for the corresponding explanation candidate and an average value of the values of the plurality of explanation candidates; a candidate ranking module configured to generate a ranking of the plurality of explanation candidates based on the scores; and a visualization suggestion module configured to: select at least one of the explanation candidates based on the ranking; and cause a corresponding selectable explanation for each one of the selected at least one explanation candidate to be displayed to a user in a graphical user interface of a device concurrently with the current visualization.
 2. The system of claim 1, wherein the indication comprises a user-generated interrupt based on a user selection of the data point.
 3. The system of claim 1, wherein the visualization explanation module is further configured to cause the current visualization to be displayed in a first dedicated section of the graphical user interface for current visualizations, and cause the corresponding selectable explanation for each one of the selected at least one explanation candidate to be displayed in a second dedicated section of the graphical user interface for explanations.
 4. The system of claim 3, wherein the visualization explanation module is further configured to: detect a user selection of the corresponding selectable explanation of one of the selected at least one explanation candidate; and cause a graphical representation corresponding to the selected explanation to be displayed as a subsequent current visualization in the first dedicated section.
 5. The system of claim 1, wherein the current visualization comprises a chart.
 6. The system of claim 1, wherein each measure of the plurality of data points comprises a numeric value.
 7. The system of claim 1, wherein each corresponding value for the plurality of explanation candidates comprises a maximum value for the corresponding dimension context of the corresponding explanation candidate.
 8. The system of claim 1, wherein the candidate determination module is further configured to apply a tuple of the selected data point of the current visualization as a filter in generating the plurality of explanation candidates.
 9. A computer-implemented method comprising: receiving an indication of a selected data point of a current visualization, the selected data point being one of a plurality of data points of the current visualization, the current visualization comprising a graphical representation of the plurality of data points, each one of the plurality of data points having a corresponding dimension and measure; determining a context of the selected data point based on the corresponding dimension of the data point; generating a plurality of explanation candidates based on the context of the selected data point, each one of the plurality of exploration candidates having a different dimension context that is within the context of the selected data point and a corresponding value for the dimension context; for each one of the plurality of explanation candidates, generating, by a machine having a memory and at least one processor, a corresponding score based on a difference between the corresponding value for the corresponding explanation candidate and an average value of all of the corresponding values of the plurality of explanation candidates; generating a ranking of the plurality of explanation candidates based on the scores; selecting at least one of the explanation candidates based on the ranking; and causing a corresponding selectable explanation for each one of the selected at least one explanation candidate to be displayed to a user in a graphical user interface of a device concurrently with the current visualization.
 10. The method of claim 9, wherein receiving the indication comprises receiving a user-generated interrupt comprising the indication, the user-generated interrupt being based on a user selection of the data point.
 11. The method of claim 9, wherein the current visualization is caused to be displayed in a first dedicated section of the graphical user interface for current visualizations, and the corresponding selectable explanation for each one of the selected at least one explanation candidate is caused to be displayed in a second dedicated section of the graphical user interface for explanations.
 12. The method of claim 11, further comprising: detecting a user selection of the corresponding selectable explanation of one of the selected at least one explanation candidate; and causing a graphical representation corresponding to the selected explanation to be displayed as a subsequent current visualization in the first dedicated section.
 13. The method of claim 9, wherein the current visualization comprises a chart.
 14. The method of claim 9, wherein each measure of the plurality of data points comprises a numeric value.
 15. The method of claim 9, wherein each corresponding value for the plurality of explanation candidates comprises a maximum value for the corresponding dimension context of the corresponding explanation candidate.
 16. The method of claim 9, wherein generating the plurality of explanation candidates comprises applying a tuple of the selected data point of the current visualization as a filter.
 17. A non-transitory machine-readable storage medium, tangibly embodying a set of instructions that, when executed by at least one processor, causes the at least one processor to perform a set of operations comprising: receiving an indication of a selected data point of a current visualization, the selected data point being one of a plurality of data points of the current visualization, the current visualization comprising a graphical representation of the plurality of data points, each one of the plurality of data points having a corresponding dimension and measure; determining a context of the selected data point based on the corresponding dimension of the data point; generating a plurality of explanation candidates based on the context of the selected data point, each one of the plurality of exploration candidates having a different dimension context that is within the context of the selected data point and a corresponding value for the dimension context; for each one of the plurality of explanation candidates, generating, by a machine having a memory and at least one processor, a corresponding score based on a difference between the corresponding value for the corresponding explanation candidate and an average value of all of the corresponding values of the plurality of explanation candidates; generating a ranking of the plurality of explanation candidates based on the scores; selecting at least one of the explanation candidates based on the ranking; and causing a corresponding selectable explanation for each one of the selected at least one explanation candidate to be displayed to a user in a graphical user interface of a device concurrently with the current visualization.
 18. The storage medium of claim 17, wherein receiving the indication comprises receiving a user-generated interrupt comprising the indication, the user-generated interrupt being based on a user selection of the data point.
 19. The storage medium of claim 17, wherein the current visualization is caused to be displayed in a first dedicated section of the graphical user interface for current visualizations, and the corresponding selectable explanation for each one of the selected at least one explanation candidate is caused to be displayed in a second dedicated section of the graphical user interface for explanations.
 20. The storage medium of claim 19, further comprising: detecting a user selection of the corresponding selectable explanation of one of the selected at least one explanation candidate; and causing a graphical representation corresponding to the selected explanation to be displayed as a subsequent current visualization in the first dedicated section. 